Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import load_learner
+from fastai import *
+import torch
+import os
+from PIL import Image
+
+model_path = 'multi_target_resnet18.pkl'
+model = load_learner(model_path)
+
+def result(path):    
+    pred,_,probability = model.predict(path)
+    arr = ['Name','Status','Disease Name']
+    vals = ['', '', '']
+
+    names = ['Maple', 'Banana', 'Cucumber', 'Mango', 'Maple', 'Pepper', 'Rose', 'Tomato']
+    status = ['diseased', 'no disease found']
+
+    for x in pred:
+      if x in names:
+        vals[0] = x.capitalize()
+      elif x in status:
+        vals[1] = x.capitalize()
+      elif x == 'healthy':
+        vals[2] = 'None'
+      else:
+        vals[2] = x.capitalize()
+      
+    return f'{arr[0]}:\t{vals[0]}\n{arr[1]}:\t{vals[1]}\n{arr[2]}:\t{vals[2]}\n'
+
+path = 'test-images'
+
+image_path = []
+
+for i in os.listdir(path):
+  image_path.append(path+i) 
+
+image = gr.inputs.Image(shape =(300,300))
+label = gr.outputs.Label()
+
+iface = gr.Interface(fn=result, inputs=image, outputs='text', examples = image_path)
+iface.launch(inline = False)